Abstract
For the response acquisition of the structure section measuring points, the method of identifying the structural stiffness parameters is developed by using the extended Kalman filter. The state equation of structural system parameter is a nonlinear equation. Dispersing the structural dynamic equation by using Newmark-βmethod, the state transition matrix of discrete state equation is deduced and the solution of discrete state equation is simplified. The numerical simulation shows that the error of structural recognition doesnot exceed 5% when the noise level is 3%. It meets the requirements of the error limit of the engineering structure, which indicates that the derivation described in this paper has the robustness for the structural stiffness recognition. Shear structure parameter identification examples illustrate its applicability, and the method can also be used to identify physical parameters of large structure.
Highlights
The dynamic response of structure which is developed in recent years is used to recognize the structural damage
Given the system initial value y0, the initial value of process noise covariance matrix P0, and observation noise covariance matrix R, discrete augmented state vector ŷk+1 and covariance matrix Pk+1 can be estimated according to the extended Kalman filter method
The paper using the method of Newmark-β disperses the equations of motion and deduces the state transition equation containing the stiffness parameters to be identified
Summary
The dynamic response of structure which is developed in recent years is used to recognize the structural damage. Damage identification is based on structural vibration, and the basic principle is structural modal parameter (natural frequency, mode shape, etc.) as the function of the structural physical characteristics (mass, damping, and stiffness), and so the change of physical characteristics will cause the change in system dynamic response [3,4,5]. Another important property which the ideal damage identification method should have is to be able to distinguish the differences of the two deviations caused by structural modeling error and structural damage. With the increase of noise level, convergence time of identified value to the true value elongates and error increases gradually but within the acceptable scope of the project, which shows algorithm has certain robustness to noise
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